125 citations · 366 across the 33 of their papers we have counts for
35 papers
Generalized Decision Transformer for Offline Hindsight Information Matching
Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu
How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for p…
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following
Kazutoshi Shinoda, Yuki Takezawa, Masahiro Suzuki +2
An interactive instruction following task has been proposed as a benchmark for learning to map natural language instructions and first-person vision into sequences of actions to in…
AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages
Machel Reid, Junjie Hu, Graham Neubig +1
Reproducible benchmarks are crucial in driving progress of machine translation research. However, existing machine translation benchmarks have been mostly limited to high-resource…
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
There is considerable interest in designing meta-reinforcement learning (meta-RL) algorithms, which enable autonomous agents to adapt new tasks from small amount of experience. In…
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima +2
Recently many algorithms were devised for reinforcement learning (RL) with function approximation. While they have clear algorithmic distinctions, they also have many implementatio…
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno +4
Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In…
Group Equivariant Conditional Neural Processes
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai +2
We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural proce…
Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks
Takumi Watanabe, Hiroki Takahashi, Goh Sato +3
This paper introduces our methodology to estimate sidewalk accessibilities from wheelchair behavior via a triaxial accelerometer in a smartphone installed under a wheelchair seat.…
Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers
Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo
Transformers have shown improved performance when compared to previous architectures for sequence processing such as RNNs. Despite their sizeable performance gains, as recently sug…
VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling
Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo
In this paper, we tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases. Existing approaches for this task are discriminat…
Epipolar-Guided Deep Object Matching for Scene Change Detection
Kento Doi, Ryuhei Hamaguchi, Shun Iwase +3
This paper describes a viewpoint-robust object-based change detection network (OBJ-CDNet). Mobile cameras such as drive recorders capture images from different viewpoints each time…
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo +2
Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that…
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Edison Marrese-Taylor, Cristian Rodriguez-Opazo, Jorge A. Balazs +2
Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal…
Combining Pretrained High-Resource Embeddings and Subword Representations for Low-Resource Languages
Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo
The contrast between the need for large amounts of data for current Natural Language Processing (NLP) techniques, and the lack thereof, is accentuated in the case of African langua…
Character-level Japanese Text Generation with Attention Mechanism for Chest Radiography Diagnosis
Kenya Sakka, Kotaro Nakayama, Nisei Kimura +6
Chest radiography is a general method for diagnosing a patient's condition and identifying important information; therefore, radiography is used extensively in routine medical prac…
Variational Inference for Learning Representations of Natural Language Edits
Edison Marrese-Taylor, Machel Reid, Yutaka Matsuo
Document editing has become a pervasive component of the production of information, with version control systems enabling edits to be efficiently stored and applied. In light of th…
An Edit-centric Approach for Wikipedia Article Quality Assessment
Edison Marrese-Taylor, Pablo Loyola, Yutaka Matsuo
We propose an edit-centric approach to assess Wikipedia article quality as a complementary alternative to current full document-based techniques. Our model consists of a main class…
Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study
Jorge A. Balazs, Yutaka Matsuo
In this paper we study how different ways of combining character and word-level representations affect the quality of both final word and sentence representations. We provide stron…
Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Learning domain-invariant representation is a dominant approach for domain generalization (DG), where we need to build a classifier that is robust toward domain shifts. However, pr…
Deep contextualized word representations for detecting sarcasm and irony
Suzana Ilić, Edison Marrese-Taylor, Jorge A. Balazs +1
Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encomp…
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
Jorge A. Balazs, Edison Marrese-Taylor, Yutaka Matsuo
In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2 place out of 26 teams with a test macro F1 scor…
Learning to Automatically Generate Fill-In-The-Blank Quizzes
Edison Marrese-Taylor, Ai Nakajima, Yutaka Matsuo +1
In this paper we formalize the problem automatic fill-in-the-blank question generation using two standard NLP machine learning schemes, proposing concrete deep learning models for…
IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets
Edison Marrese-Taylor, Suzana Ilic, Jorge A. Balazs +2
In this paper we introduce our system for the task of Irony detection in English tweets, a part of SemEval 2018. We propose representation learning approach that relies on a multi-…
Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Recent advances in neural autoregressive models have improve the performance of speech synthesis (SS). However, as they lack the ability to model global characteristics of speech (…